RNA-Seq: differential expression analysis of controls (literature)
Libraries required
Data
Expression comparison
PND7 vs PND8
lab.pnd8 <- log2(
sort(
rowMeans(
lab@gene.counts[, grep(pattern = "PND8", x = colnames(lab@gene.counts), value = T)]
),
decreasing = TRUE
)
)[1:5000]
lit.pnd7 <- log2(
sort(
rowMeans(
lit@gene.counts[, grep(pattern = "PND7", x = colnames(lit@gene.counts), value = T)]
),
decreasing = TRUE
)
)[1:5000]
genes <- intersect(names(lab.pnd8), names(lit.pnd7))
x <- lab.pnd8[genes]
y <- lit.pnd7[genes]
fit <- lm(y ~ x)
plot_ly() %>%
add_trace(x = x, y = y, mode = "markers") %>%
add_trace(x = x, y = predict(fit), mode = "lines") %>%
layout(
xaxis = list(title = "PND8 (Lab)"),
yaxis = list(title = "PND7 (Literature)"),
title = "SC controls expression: Lab vs Literature"
)PND14 vs PND15
lab.pnd15 <- log2(
sort(
rowMeans(
lab@gene.counts[, grep(pattern = "PND15", x = colnames(lab@gene.counts), value = T)]
),
decreasing = TRUE
)
)[1:5000]
lit.pnd14 <- log2(
sort(
# rowMeans(
lit@gene.counts[, grep(pattern = "PND14", x = colnames(lit@gene.counts), value = T)],
decreasing = TRUE
)
)[1:5000]
genes <- intersect(names(lab.pnd15), names(lit.pnd14))
x <- lab.pnd15[genes]
y <- lit.pnd14[genes]
fit <- lm(y ~ x)
plot_ly() %>%
add_trace(x = x, y = y, mode = "markers") %>%
add_trace(x = x, y = predict(fit), mode = "lines") %>%
layout(
xaxis = list(title = "PND15 (Lab)"),
yaxis = list(title = "PND14 (Literature)"),
title = "SC controls expression: Lab vs Literature"
)PNW8 vs Adult
lab.adult <- log2(
sort(
rowMeans(
lab@gene.counts[, grep(pattern = "Adult", x = colnames(lab@gene.counts), value = T)]
),
decreasing = TRUE
)
)[1:5000]
lit.pnw8 <- log2(
sort(
# rowMeans(
lit@gene.counts[, grep(pattern = "PNW8", x = colnames(lit@gene.counts), value = T)],
decreasing = TRUE
)
)[1:5000]
genes <- intersect(names(lab.adult), names(lit.pnw8))
x <- lab.adult[genes]
y <- lit.pnw8[genes]
fit <- lm(y ~ x)
plot_ly() %>%
add_trace(x = x, y = y, mode = "markers") %>%
add_trace(x = x, y = predict(fit), mode = "lines") %>%
layout(
xaxis = list(title = "Adult (Lab)"),
yaxis = list(title = "PNW8 (Literature)"),
title = "SC controls expression: Lab vs Literature"
)Heatmap
genes <- data.frame(
Category = c(
rep("Germ cell licensing", 5),
rep("Undifferentiated Spermatogonia markers", 12),
"Sertoli cell marker",
"Leyidig cell marker",
"Translational initiation factor",
"Translational initiation factor",
"Housekeeping gene"
),
Genes = c(
"Nanos2", "Nanos3", "Dazl", "Ddx4", "Dmrt1",
"Taf4b", "Neurog3", "Utf1", "Id4", "Zbtb16", "Pou5f1",
"Lin28a", "Bcl6b", "Sall4", "Sohlh1", "Foxo1", "Gfra1",
"Gata4", "Lhcgr",
"Eif4h", "Eif4e", "Gapdh"
), stringsAsFactors = F
)
rownames(genes) <- genes$Genes
tab <- data.frame(lab@gene.counts, lit@gene.counts)
tab <- DGEList(tab)
tab <- calcNormFactors(tab)
cpm <- cpm(tab, log = T)
tab <- cpm[genes$Genes, ]
tab[mapply(is.infinite, tab)] <- 0
tab <- tab[, c(
grep(pattern = "PND7", x = colnames(tab), value = T),
grep(pattern = "PND8", x = colnames(tab), value = T),
grep(pattern = "PND14", x = colnames(tab), value = T),
grep(pattern = "PND15", x = colnames(tab), value = T),
grep(pattern = "PNW8", x = colnames(tab), value = T),
grep(pattern = "Adult", x = colnames(tab), value = T)
)]
annoCol <- rbind(lab@phenoData, lit@phenoData)
pheatmap(
mat = tab, color = viridis(100), border_color = NA,
cluster_cols = F, cluster_rows = F,
gaps_col = c(10, 20), gaps_row = c(5, 17, 18, 19, 21),
annotation_row = genes[, 1, drop = F],
annotation_col = annoCol[,1, drop = F]
)
SessionInfo
## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.1 (2019-07-05)
## os Ubuntu 16.04.6 LTS
## system x86_64, linux-gnu
## ui X11
## language (EN)
## collate de_DE.UTF-8
## ctype de_DE.UTF-8
## tz Europe/Zurich
## date 2019-12-16
##
## ─ Packages ───────────────────────────────────────────────────────────────────
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